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Open AccessArticle

Local Community Detection in Dynamic Graphs Using Personalized Centrality

School of Computational Science and Engineering, Georgia Tech, Atlanta, GA 30332, USA
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in ICCS 2017, ASONAM 2017, and GABB 2016.
Algorithms 2017, 10(3), 102;
Received: 31 May 2017 / Revised: 22 August 2017 / Accepted: 23 August 2017 / Published: 29 August 2017
(This article belongs to the Special Issue Algorithms for Community Detection in Complex Networks)
Analyzing massive graphs poses challenges due to the vast amount of data available. Extracting smaller relevant subgraphs allows for further visualization and analysis that would otherwise be too computationally intensive. Furthermore, many real data sets are constantly changing, and require algorithms to update as the graph evolves. This work addresses the topic of local community detection, or seed set expansion, using personalized centrality measures, specifically PageRank and Katz centrality. We present a method to efficiently update local communities in dynamic graphs. By updating the personalized ranking vectors, we can incrementally update the corresponding local community. Applying our methods to real-world graphs, we are able to obtain speedups of up to 60× compared to static recomputation while maintaining an average recall of 0.94 of the highly ranked vertices returned. Next, we investigate how approximations of a centrality vector affect the resulting local community. Specifically, our method guarantees that the vertices returned in the community are the highly ranked vertices from a personalized centrality metric. View Full-Text
Keywords: local community detection; dynamic graphs; personalized centrality metrics local community detection; dynamic graphs; personalized centrality metrics
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Nathan, E.; Zakrzewska, A.; Riedy, J.; Bader, D.A. Local Community Detection in Dynamic Graphs Using Personalized Centrality. Algorithms 2017, 10, 102.

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